text: import numpy as np
  from gym.envs.mujoco import mujoco_env
  from gym import utils
  
  
  DEFAULT_CAMERA_CONFIG = {
      'trackbodyid': 1,
      'distance': 4.0,
      'lookat': np.array((0.0, 0.0, 2.0)),
      'elevation': -20.0,
  }
  
  
  def mass_center(model, sim):
      mass = np.expand_dims(model.body_mass, axis=1)
      xpos = sim.data.xipos
      return (np.sum(mass * xpos, axis=0) / np.sum(mass))[0:2].copy()
  
  
  class HumanoidEnv(mujoco_env.MujocoEnv, utils.EzPickle):
      def __init__(self,
                   xml_file='humanoid.xml',
                   forward_reward_weight=0.25,
                   ctrl_cost_weight=0.1,
                   contact_cost_weight=5e-7,
                   contact_cost_range=(-np.inf, 10.0),
                   healthy_reward=5.0,
                   terminate_when_unhealthy=True,
                   healthy_z_range=(1.0, 2.0),
                   reset_noise_scale=1e-2,
                   exclude_current_positions_from_observation=True,
                   rgb_rendering_tracking=True):
          utils.EzPickle.__init__(**locals())
  
          self._forward_reward_weight = forward_reward_weight
          self._ctrl_cost_weight = ctrl_cost_weight
          self._contact_cost_weight = contact_cost_weight
          self._contact_cost_range = contact_cost_range
          self._healthy_reward = healthy_reward
          self._terminate_when_unhealthy = terminate_when_unhealthy
          self._healthy_z_range = healthy_z_range
  
          self._reset_noise_scale = reset_noise_scale
  
          self._exclude_current_positions_from_observation = (
              exclude_current_positions_from_observation)
  
          mujoco_env.MujocoEnv.__init__(self, xml_file, 5, rgb_rendering_tracking=rgb_rendering_tracking)
  
      @property
      def healthy_reward(self):
          return float(
              self.is_healthy
              or self._terminate_when_unhealthy
          ) * self._healthy_reward
  
      def control_cost(self, action):
          control_cost = self._ctrl_cost_weight * np.sum(
              np.square(self.sim.data.ctrl))
          return control_cost
  
      @property
      def contact_cost(self):
          contact_forces = self.sim.data.cfrc_ext
          contact_cost = self._contact_cost_weight * np.sum(
              np.square(contact_forces))
          min_cost, max_cost = self._contact_cost_range
          contact_cost = np.clip(contact_cost, min_cost, max_cost)
          return contact_cost
  
      @property
      def is_healthy(self):
          min_z, max_z = self._healthy_z_range
          is_healthy = min_z < self.sim.data.qpos[2] < max_z
  
          return is_healthy
  
      @property
      def done(self):
          done = ((not self.is_healthy)
                  if self._terminate_when_unhealthy
                  else False)
          return done
  
      def _get_obs(self):
          position = self.sim.data.qpos.flat.copy()
          velocity = self.sim.data.qvel.flat.copy()
  
          com_inertia = self.sim.data.cinert.flat.copy()
          com_velocity = self.sim.data.cvel.flat.copy()
  
          actuator_forces = self.sim.data.qfrc_actuator.flat.copy()
          external_contact_forces = self.sim.data.cfrc_ext.flat.copy()
  
          if self._exclude_current_positions_from_observation:
              position = position[2:]
  
          return np.concatenate((
              position,
              velocity,
              com_inertia,
              com_velocity,
              actuator_forces,
              external_contact_forces,
          ))
  
      def step(self, action):
          xy_position_before = mass_center(self.model, self.sim)
          self.do_simulation(action, self.frame_skip)
          xy_position_after = mass_center(self.model, self.sim)
  
          xy_velocity = (xy_position_after - xy_position_before) / self.dt
          x_velocity, y_velocity = xy_velocity
  
          x_velocity = ((xy_position_after[0] - xy_position_before[0])
                        / self.model.opt.timestep)
  
          ctrl_cost = self.control_cost(action)
          contact_cost = self.contact_cost
  
          forward_reward = self._forward_reward_weight * x_velocity
          healthy_reward = self.healthy_reward
  
          rewards = forward_reward + healthy_reward
          costs = ctrl_cost + contact_cost
  
          observation = self._get_obs()
          # reward = rewards - costs
          """
          Sparse rewards
          """
          reward = int( abs(xy_position_after[0]) >= 0.6 )
  
          done = self.done
          info = {
              'reward_linvel': forward_reward,
              'reward_quadctrl': -ctrl_cost,
              'reward_alive': healthy_reward,
              'reward_impact': -contact_cost,
  
              'x_position': xy_position_after[0],
              'y_position': xy_position_after[1],
              'distance_from_origin': np.linalg.norm(xy_position_after, ord=2),
  
              'x_velocity': x_velocity,
              'y_velocity': y_velocity,
              'forward_reward': forward_reward,
          }
  
          return observation, reward, done, info
  
      def reset_model(self):
          noise_low = -self._reset_noise_scale
          noise_high = self._reset_noise_scale
  
          qpos = self.init_qpos + self.np_random.uniform(
              low=noise_low, high=noise_high, size=self.model.nq)
          qvel = self.init_qvel + self.np_random.uniform(
              low=noise_low, high=noise_high, size=self.model.nv)
          self.set_state(qpos, qvel)
  
          observation = self._get_obs()
          return observation
  
      def viewer_setup(self):
          for key, value in DEFAULT_CAMERA_CONFIG.items():
              if isinstance(value, np.ndarray):
                  getattr(self.viewer.cam, key)[:] = value
              else:
                  setattr(self.viewer.cam, key, value)
  